The JEPA Architecture
The Joint Embedding Predictive Architecture (JEPA), developed by Meta AI, is a non-generative paradigm for model training. Unlike standard models that predict tokens or pixels, JEPA learns to predict latent representations of missing information. This approach prioritizes semantic consistency over high-frequency surface details.
Components of Predictive Agents
- World Model: An internal module that predicts state transitions resulting from specific actions.
- Cost Module: A function that evaluates the desirability or safety of predicted future states.
- Actor: A policy engine that proposes actions to optimize state transitions according to the cost module.
Theoretical Impact
World Models facilitate mental simulation and look-ahead planning. By simulating actions within an internal model before execution, agents can verify outcomes against constraints—a critical capability for deliberative reasoning systems.